初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/Ross-640-BMath-1.5B
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-1.5B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+- math
+- trl
+- SFT
+---
+
+![89.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/vxacZQLTe3BR8F7Np_CQU.png)
+
+# **Ross-640-BMath-1.5B**
+
+> **Ross-640-BMath-1.5B** is an **experimental, high-precision math explanation model** fine-tuned on **Qwen2-1.5B**, designed to provide **step-by-step mathematical derivations** and **detailed concept explanations** across a wide range of mathematical domains. It is **not optimized for general reasoning or conversation**, and focuses primarily on **structured, non-reasoning math workflows** including algebra, calculus, number theory, and combinatorics.
+
+> \[!note]
+> GGUF: [https://huggingface.co/prithivMLmods/Ross-640-BMath-1.5B-GGUF](https://huggingface.co/prithivMLmods/Ross-640-BMath-1.5B-GGUF)
+
+---
+
+## **Key Features**
+
+1. **Hard Math Concept Focus**
+   Specializes in **algebra**, **calculus**, **combinatorics**, **linear algebra**, **number theory**, and more—delivering fine-tuned, low-latency outputs ideal for **math-intensive applications**.
+
+2. **Step-by-Step Explanations**
+   Emphasizes **procedural clarity** over abstract reasoning, offering structured, educational breakdowns of mathematical problems and derivations.
+
+3. **Symbolic Computation & Annotation**
+   Outputs include LaTeX-compatible syntax, inline math symbols, and clear annotation to support academic and technical workflows.
+
+4. **Educational Utility**
+   Optimized for **learning and teaching**, providing clear responses to mathematical queries with minimal noise or conversational drift.
+
+5. **Lightweight Architecture**
+   Built on Qwen2-1.5B and fine-tuned for **efficiency and precision**, making it suitable for deployment in **resource-constrained environments**, educational tools, or math-centric chat interfaces.
+
+---
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Ross-640-BMath-1.5B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Explain step-by-step how to integrate (x^2 + 1)/(x^3 + 3x) dx."
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant skilled in solving complex math problems with clear and structured steps."},
+    {"role": "user", "content": prompt}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+---
+
+## **Intended Use**
+
+* Detailed mathematical explanations and problem-solving
+* Education-focused tutoring and math derivation tools
+* Math-focused applications and formula documentation
+* Symbolic derivations and LaTeX generation
+* Integration with learning platforms and academic software
+
+---
+
+## **Limitations**
+
+* Not suitable for general-purpose conversation or reasoning tasks
+* Context length constraints may limit effectiveness on large proofs
+* May struggle with non-mathematical or open-ended creative tasks
+* Experimental: Fine-tuned primarily for **explanation clarity**, not deep symbolic reasoning or formal proof validation